Prospective Graduate Students
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Cyber and Software Systems
Our faculty and affiliates engage in a broad range of collaborative research projects with our academic, government, and industry partners. While most of our projects expand beyond traditional disciplinary divides, projects in this area are closely related to computer science, electrical engineering, and software engineering.
Project abstracts
Cybersecurity with Secure Elements
Lead: Bertrand Cambou
Keywords: Cybersecurity, physically unclonable functions, true random number generators, ReRAM
This research project is focused on hardware-software solutions based on secure elements (micro-controllers with embedded secure memories), components that are widely distributed on terminals, mobile devices, banking cards, ID/passports, and Internet of things.
SEGA Cyberinfrastructure
Lead: Paul Flikkema
Keywords: Cyber-physical systems, ecological informatics, real-time streaming systems
SEGA cyberinfrastructure (CI) will form a critical component of SEGA’s network of experimental gardens. It will be a fully integrated cyber-physical design, with physical control of temperature across a 1500-m elevational gradient and cyber control of water availability using a sensor-actuator network.
UAV Tracking System for Monitoring Wildlife
Lead: Michael Shafer
Keywords: Unmanned aerial vehicle, radio telemetry, wildlife tracking
Current methods of locating and tracking small tagged animals are hampered by the inaccessibility of their habitats. The high costs, risk to human safety, and small sample sizes resulting from current radio telemetry methods limit our understanding of the movement and behaviors of many species. UAV-based technologies promise to revolutionize a range of ecological field study paradigms due to the ability of a sensing platform to fly in close proximity to rough terrain at very low cost.
Design Challenges and Stories: Reflective Design Learning
Lead: John Georgas
Keywords: Software engineering, pedagogy, reflective design learning
This project is investigating strategies to better foster design learning in undergraduate computer science courses and centers on constructivist learning theories, particularly reflection-based learning. Our approach focuses on the centrality of structured reflection over a design problem — called a design challenge — to result in a reflective narrative — called a design story.
Runtime Architectural Visualization
Lead: John Georgas
Keywords: Software engineering, runtime visualization
This newly-initiated project is focused on developing the next generation of architectural visualization techniques that both integrate animated elements showing runtime system behavior and also explore fundamentally different types of visualization approaches that leverage color and three-dimensional shapes to better support understanding the behavior and interactions of software module.
Analysis and Certification of Parallel Programs
Lead: Frédéric Loulergue
Keywords: Parallel and concurrent programming, deductive verification, interactive theorem proving
With the current generalization of parallel architectures and increasing requirement of parallel computation arises the concern of applying formal methods, which allow specifications of parallel and distributed programs to be precisely stated and the conformance of an implementation to be verified using mathematical techniques.
High-Level Parallel Programming
Lead: Frédéric Loulergue
Keywords: Programming languages, parallel programming, scalable computing
If parallel programming is to become as widespread as sequential programming, the languages supporting it should incorporate all the standard abstraction mechanisms including higher order functions, recursion, pattern matching, etc. Yet for such languages to be practical scalable programming tools, abstraction should not come at the price of predictable performance.
Software Engineering at Scale: Leveraging Open Source Software (big) data to improve software development
Lead: Marco Gerosa, Igor Steinmacher
Keywords: Big data, sociotechnical aspects, collaborative work, machine learning, education
Open Source Software (OSS) projects represent a major economic driving force. Thousands of openly available projects, comprising years of software development, produce systems largely used in all kinds of business. Our research aims to: (i) scientifically analyze the large volume of data (big data) associated with these projects as well as to collect subjective data from developers to understand how to better engineer software; (ii) to build tools to support these developers; and (iii) to leverage the participation of students and new developers in this universe of projects.